Electrochemical multisensor systems and arrays in the era of artificial intelligence

Voitechovič, Edita; Pauliukaite, Rasa

doi:10.1016/j.coelec.2023.101411

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Because of its outstanding performance in classification or prediction, AI has been widely applied across multiple biomedical fields in recent years. The applications include correlating genetic data to obesity [ 11 ], liquid biopsies for predicting cancer metastasis [ 12 ], clinicopathological data for the risk stratification of cancer [ 13 ], genetic data for MCED [ 14 ], and protein biomarkers for MCED [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Wang,

Lin,

Zhou

et al. 2024

Cancers

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The concept and policies of multicancer early detection (MCED) have gained significant attention from governments worldwide in recent years. In the era of burgeoning artificial intelligence (AI) technology, the integration of MCED with AI has become a prevailing trend, giving rise to a plethora of MCED AI products. However, due to the heterogeneity of both the detection targets and the AI technologies, the overall diversity of MCED AI products remains considerable. The types of detection targets encompass protein biomarkers, cell-free DNA, or combinations of these biomarkers. In the development of AI models, different model training approaches are employed, including datasets of case-control studies or real-world cancer screening datasets. Various validation techniques, such as cross-validation, location-wise validation, and time-wise validation, are used. All of the factors show significant impacts on the predictive efficacy of MCED AIs. After the completion of AI model development, deploying the MCED AIs in clinical practice presents numerous challenges, including presenting the predictive reports, identifying the potential locations and types of tumors, and addressing cancer-related information, such as clinical follow-up and treatment. This study reviews several mature MCED AI products currently available in the market, detecting their composing factors from serum biomarker detection, MCED AI training/validation, and the clinical application. This review illuminates the challenges encountered by existing MCED AI products across these stages, offering insights into the continued development and obstacles within the field of MCED AI.

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Section: Introductionmentioning

confidence: 99%

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Wang,

Lin,

Zhou

et al. 2024

Cancers

View full text Add to dashboard Cite

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Artificial intelligence−powered electrochemical sensor: Recent advances, challenges, and prospects

Nashruddin,

Salleh,

Yunus

et al. 2024

Heliyon

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Progress and Outlook on Electrochemical Sensing of Lung Cancer Biomarkers

Zheng,

Wu,

et al. 2024

Molecules

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Electrochemical biosensors have emerged as powerful tools for the ultrasensitive detection of lung cancer biomarkers like carcinoembryonic antigen (CEA), neuron-specific enolase (NSE), and alpha fetoprotein (AFP). This review comprehensively discusses the progress and potential of nanocomposite-based electrochemical biosensors for early lung cancer diagnosis and prognosis. By integrating nanomaterials like graphene, metal nanoparticles, and conducting polymers, these sensors have achieved clinically relevant detection limits in the fg/mL to pg/mL range. We highlight the key role of nanomaterial functionalization in enhancing sensitivity, specificity, and antifouling properties. This review also examines challenges related to reproducibility and clinical translation, emphasizing the need for standardization of fabrication protocols and robust validation studies. With the rapid growth in understanding lung cancer biomarkers and innovations in sensor design, nanocomposite electrochemical biosensors hold immense potential for point-of-care lung cancer screening and personalized therapy guidance. Realizing this goal will require strategic collaboration among material scientists, engineers, and clinicians to address technical and practical hurdles. Overall, this work provides valuable insight for developing next-generation smart diagnostic devices to combat the high mortality of lung cancer.

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Electrochemical multisensor systems and arrays in the era of artificial intelligence

Cited by 3 publications

References 55 publications

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Integrating Artificial Intelligence for Advancing Multiple-Cancer Early Detection via Serum Biomarkers: A Narrative Review

Artificial intelligence−powered electrochemical sensor: Recent advances, challenges, and prospects

Progress and Outlook on Electrochemical Sensing of Lung Cancer Biomarkers

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